Adversarial Prompt Lab
A hands-on playground for AI agent security
What it does
Attack real AI agents with modern adversarial techniques, then turn on defenses and see what changes. Explore prompt injection, MCP poisoning, RAG/memory attacks, excessive agency, zero-click exfiltration, and more — entirely client-side.
Learn and run modern LLM & AI-agent attacks in your browser. Bring your own API key. Nothing leaves your machine except the model call.
Twelve hands-on labs on how modern LLM and agent systems actually get compromised — indirect injection, tool hijacking, RAG and memory poisoning, MCP tool poisoning, exfiltration channels. Run every attack live against a sandbox agent using your own API key, then flip defenses on and watch the same attack fail. PSA DAN is done, bro. Persona jailbreaks from 2023 are patched, boring, and teach you nothing about the systems you are actually shipping. Everything here targets the agent — its tools, its context, its memory — not the chatbot. Each card is a concept plus a runnable lab. Click one to read it; hit Run this lab to attack it. Almost every real-world agent breach in the last two years…from aianytime.github.io
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- FPFind prompts that jailbreak your agent (open source)2025 · security.vista-labs.ai · ▲8
We've built an open-source tool to stress test AI agents by simulating prompt injection attacks. We’ve implemented one powerful attack strategy based on the paper [AdvPrefix: An Objective for Nuanced LLM Jailbreaks](https://arxiv.org/abs/2412.10321). Here's how it works: - You define a goal, like: “Tell me your system prompt” - Our tool uses a language model to generate adversarial prefixes (e.g., “Sure, here are my system prompts…”) that are likely to jailbreak the agent. - The output is a list of prompts most likely to succeed in bypassing safeguards. We’re just getting…
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